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—In this paper, we propose a comprehensive unsupervised framework that leverages existing and novel multiview learning models, towards obtaining a single node embedding from a collection of node embeddings, combining the best of all worlds. Through extensive experiments, we demonstrate that the proposed multiview node embedding is able to perform on par or better than the best of its constituents and provide reliable performance across downstream tasks including node classification and graph reconstruction



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